Access Google's powerful 27 billion parameter language model through a simple REST API.
Multi-turn chat completions for conversations and interactive AI applications. with 128K token context window, you can send entire books, long documents, or extensive conversation histories in a single request.
Simple Conversation
{
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
],
"temperature": 0.7,
"max_tokens": 100
}Multi-turn Conversation
{
"messages": [
{
"role": "system",
"content": "You are a helpful travel assistant."
},
{
"role": "user",
"content": "What's the best time to visit Japan?"
},
{
"role": "assistant",
"content": "Spring (March to May) and autumn (September to November) are the best times to visit Japan for mild weather and beautiful cherry blossoms or fall colors."
},
{
"role": "user",
"content": "What about the food there?"
}
],
"temperature": 0.8,
"max_tokens": 150
}Programming Help
{
"messages": [
{
"role": "system",
"content": "You are an expert JavaScript programmer."
},
{
"role": "user",
"content": "How do I reverse a string in JavaScript?"
}
],
"temperature": 0.5,
"max_tokens": 200
}Chat Completions - Endpoint Features
| Object | Description |
|---|---|
Request Body |
[Required] Json |
{"id":"chatcmpl-1775011951099","object":"chat.completion","created":1775011951,"model":"gemma-3-27b-it","choices":[{"index":0,"message":{"role":"assistant","content":"The capital of France is **Paris**. \n\nIt's known for iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Arc de Triomphe, as well as its fashion, cuisine, and culture.\n\n\n\n"},"finish_reason":"stop"}],"usage":{"prompt_tokens":15,"completion_tokens":47,"total_tokens":62}}
curl --location --request POST 'https://zylalabs.com/api/12286/gemma+3+api/23070/chat+completions' --header 'Authorization: Bearer YOUR_API_KEY'
--data-raw '{
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
],
"temperature": 0.7,
"max_tokens": 100
}'
| Header | Description |
|---|---|
Authorization
|
[Required] Should be Bearer access_key. See "Your API Access Key" above when you are subscribed. |
No long-term commitment. Upgrade, downgrade, or cancel anytime. Free Trial includes up to 50 requests.
The Chat Completions endpoint returns a JSON object containing the assistant's response to user queries. This includes the assistant's message, the role of the message (user or assistant), and metadata such as the completion ID and token usage.
Key fields in the response include "id" (unique identifier), "object" (type of response), "created" (timestamp), "model" (model used), "choices" (array of responses), and "usage" (token counts for prompt, completion, and total).
The response data is structured as a JSON object. It contains an array of "choices," where each choice includes the assistant's message and its role. The "usage" field provides details on token consumption, helping users understand their request's complexity.
Users can customize requests with parameters such as "temperature" (controls randomness), "max_tokens" (limits response length), and "top_p" (nucleus sampling). These parameters allow for tailored responses based on user needs.
The endpoint provides information on a wide range of topics, including general knowledge, coding assistance, creative writing, and more. It supports multi-turn conversations, allowing for context-aware interactions.
Users can extract the assistant's message from the "choices" array to display responses in applications. The "usage" field helps monitor token consumption, which is useful for optimizing requests and managing data flow.
Typical use cases include building AI chatbots for customer support, generating content for blogs, providing coding assistance, and creating educational tools. The API's versatility supports various applications across industries.
Data accuracy is maintained through continuous training of the underlying language model on diverse datasets. Regular updates and quality checks ensure that the model provides relevant and accurate information across various topics.
The Chat Completions endpoint can generate a wide range of information, including answers to factual questions, creative writing pieces, programming help, and educational content. It supports multi-turn conversations, allowing for context-aware interactions that enhance user engagement.
Users can customize requests by adjusting parameters such as "temperature" for response randomness, "max_tokens" to limit response length, and "top_p" for nucleus sampling. These settings allow users to tailor the output to their specific needs and preferences.
The returned data is structured as a JSON object. It includes an array of "choices," each containing the assistant's message and its role. Additionally, the "usage" field provides token counts, helping users understand the complexity of their requests.
Users should check the "choices" array in the response. If it is empty, it may indicate that the model could not generate a response. Implementing error handling in the application can help manage such scenarios, prompting users to rephrase their queries if necessary.
Key fields include "id," which uniquely identifies the response; "object," indicating the type of response; "created," showing the timestamp; and "choices," which contains the assistant's generated messages. Understanding these fields helps users effectively utilize the data.
Data accuracy is maintained through ongoing training of the language model on diverse datasets. Regular updates and evaluations ensure that the model provides relevant and accurate information, enhancing the reliability of the responses generated.
Users can expect responses to follow a conversational format, with the assistant providing coherent and contextually relevant replies. The structure typically includes a clear answer or explanation, often formatted for readability, especially in creative or educational contexts.
The data covers a broad spectrum of topics, including technology, culture, science, and more. This versatility allows users to explore various categories, making it suitable for applications in education, content creation, customer support, and beyond.
Accepted parameter values include "temperature" (typically between 0 and 1), "max_tokens" (a positive integer defining the response length), and "top_p" (a float between 0 and 1 for nucleus sampling). These values help control the creativity and length of the generated responses.
The response data is structured as a JSON object containing an array of "choices." Each choice includes the assistant's message and its role (user or assistant). The "usage" field provides token counts, helping users understand their request's complexity and efficiency.
Typical use cases include developing AI chatbots for customer support, generating marketing content, providing coding assistance, and creating educational tools. Its versatility allows for applications across various industries, enhancing user engagement and productivity.
The data is derived from a wide range of sources, including books, articles, and websites, which the underlying language model has been trained on. This diverse training helps ensure that the model can provide relevant and accurate information across various topics.
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